Manufacturers must focus on staff retention
A recent EEF skills report has confirmed that the UK manufacturing sector is in the midst of a staffing crisis. Crucially, over a third of vacant industry roles are now considered ‘hard to fill’ and 67 percent of employers cite a lack of technical skills among applicants as a main driver behind their recruitment difficulties. As the pace of technological change within the sector increases and with the flow of graduates with STEM (science, technology, engineering and maths) training in short supply, talented employees are a valuable commodity.
In order to promote business growth and retain a competitive edge, staff retention must be a priority for businesses. Selecting a remuneration package that promotes loyalty, productivity and motivates staff is essential, below are some of the most pertinent options that business leaders should consider when constructing their firm’s offering.
Share incentive schemes
The most efficient workforces are often those that have been provided with comprehensive training, to ensure they have a strong skill set and are up to date with the latest developments in production techniques, leadership and technology. However, for training expenditure to represent a good investment, keeping staff with the company on a long-term basis is key – often the more skilled an employee is, the more sought after they will be by competitors.
One such way to promote long-term staff retention is through the introduction of share incentive schemes, such as the EMI (Enterprise Management Incentive) share option scheme. This initiative allows businesses to reward staff in a tax efficient manner, by allowing them to purchase shares in the company at some point in the future. Not only is the eventual sale of these shares taxable at 10 percent, but the status of being a shareholder within the business promotes a sense of pride and ownership likely to encourage longevity.
The downside of the EMI share scheme however, is that the options or shares themselves do not hold a tangible value until they are sold, and individuals often have to wait for years to see this financial gain realised. So this solution may be best incorporated with other, more short term measures.
Executive bonus schemes
It may prove sensible to share the financial success of the business with key employees on an annual or quarterly basis, many of whom are likely to hold knowledge and experience that is essential to the firm’s ongoing success. In order to implement a bonus scheme effectively, clear and objective KPIs must be agreed upon and the associated rewards clearly communicated with staff.
Business leaders must be sure to balance the performance of individuals with the performance of the business as a whole, to ensure that they do not pay out more in bonuses than the business has made in profit. In addition, promoting the right culture is essential, and managers must be sure not to encourage the wrong behaviours but to emphasise the importance of team cohesion and group success rather than rewarding individual performance in isolation.
Often the simplest employee benefits are the most sought after. Aside from the usual cycle to work schemes and childcare vouchers, offering staff flexible working patterns can prove to be a huge carrot and significantly increase length of service, especially for those with families. These types of benefits may be extremely difficult or awkward to negotiate elsewhere and are a great tool in promoting loyalty.
While flexible shift patterns may not be suitable for all manufacturing roles, especially those on the production line, this can be interchanged with the ability to buy and sell holiday days as well as company-wide late starts or early finishes as a reward for increased productivity.
Employee ownership schemes
The average business life-cycle is shortening, and many entrepreneurial manufacturers are thinking about their exit strategy at an earlier stage. To retain key members of the leadership team and to cement their commitment to growing the business before its eventual sale, it may be possible to agree upon a management buy-out (MBO) or the adoption of an employee ownership model once the business owner is ready to leave.
This will act to encourage employees to engage with the long-term goals of the organisation and protect the business’ working culture. In addition, the promise of greater control or partial ownership is likely to increase staff retention, productivity and motivation, as well as paving the way for a smooth exit for the current owner.
Ed Hussey is director of HR services at accountancy firm Menzies LLP
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5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.